Since joining Fermilab in 2016, my scientific work has focused on the search of new physics in neutrino oscillation experiments.

As a member of the MicroBooNE Collaboration (since 2016), I am mainly interested in searching for sterile neutrinos, as well as in the development of techniques for the reconstruction and analysis of Liquid Argon Time Project Chambers data. I also joined the DUNE Collaboration, where my main interest is CP violation in the neutrino sector.

Previously I worked for about a decade on the CMS experiments, with major contributions to the Higgs boson discovery, SUSY searches and Standard Model measurements.

I served as CMS Track Reconstruction convener in 2013-2014.

Kyle Chard is a Research Associate Professor in the Department of Computer Science at the University of Chicago and Argonne National Laboratory. He has been Program Director of the Data & Computing Summer Lab since its first iteration under CDAC in 2019, and previously oversaw the Summer Internship Program ran by the former Computation Institute.

He received his Ph.D. in Computer Science from Victoria University of Wellington in 2011. He co-leads the Globus Labs research group which focuses on a broad range of research problems in data-intensive computing and research data management. He currently leads projects related to parallel programming in Python, scientific reproducibility, and elastic and cost-aware use of cloud infrastructure.

My research examines the different ways in which goals, desires and needs affect how people perceive and respond to our environment. My work draws from the traditions of cognitive neuroscience, social psychology and affective science. I use a broad range of methodological tools, including behavioral experiments, computational modeling, fMRI, pupillometry, naturalistic paradigms and network analyses. By combining different tools and perspectives, I seek to characterize motivational influences on human cognition at the psychological, computational and neural levels. One ultimate goal of this work is to identify behavioral and neural targets of intervention to improve socio-cognitive functioning.

I direct the Motivation and Cognition Neuroscience Laboratory at the University of Chicago.

Website

Yuxin Chen is an assistant professor at the Department of Computer Science at the University of Chicago. Previously, he was a postdoctoral scholar in Computing and Mathematical Sciences at Caltech, hosted by Prof. Yisong Yue. He received my Ph.D. degree in Computer Science from ETH Zurich, under the supervision of Prof. Andreas Krause. He is a recipient of the PIMCO Postdoctoral Fellowship in Computing and Mathematical Sciences, a Swiss National Science Foundation Early Postdoc.Mobility fellowship, and a Google European Doctoral Fellowship in Interactive Machine Learning.

His research interest lies broadly in probabilistic reasoning and machine learning. He is currently working on developing interactive machine learning systems that involve active learning, sequential decision making, interpretable models and machine teaching. You can find more information in my Google scholar profile.

Homepage.

I am an Assistant Professor of Computer Science at the University of Chicago. I founded and direct 3DL (threedle! ), a group of enthusiastic researchers passionate about 3D, machine learning, and visual computing. I obtained my Ph.D. in 2021 from Tel Aviv University under the supervision of Daniel Cohen-Or and Raja Giryes.

My research is focused on building artificial intelligence for 3D data, spanning the fields of computer graphics, machine learning, and computer vision. Deep learning, the most popular form of artificial intelligence, has unlocked remarkable success on structured data (such as text, images, and video), and I am interested in harnessing the potential of these techniques to enable effective operation on unstructured 3D geometric data.

We have developed a convolutional neural network designed specifically for meshes, and also explored how to learn from the internal data within a single shape (for surface reconstruction, geometric texture synthesis, and point cloud consolidation) – and I am interested in broader applications related to these areas. Additional research directions that I am aiming to explore include: intertwining human and machine-based creativity to advance our capabilities in 3D shape modeling and animation; learning with less supervision, for example to extract patterns and relationships from large shape collections; and making 3D neural networks more “interpretable/explainable”.

Julia Koschinsky is the Executive Director of the Center for Spatial Data Science at the University of Chicago and has been part of the GeoDa team for over 16 years. She has been conducting and managing research funded through federal awards of over $8 million to gain insights from the spatial dimensions of urban challenges in housing, health, and the built environment.

Tian Li is an Assistant Professor of computer science and data science. Her research centers around distributed optimization, federated learning, and trustworthy ML. She is interested in designing, analyzing, and evaluating principled learning algorithms, taking into account practical constraints, to address issues related to accuracy, scalability, trustworthiness, and their interplays. Tian received her Ph.D. in Computer Science from Carnegie Mellon University. Prior to CMU, she received her undergraduate degrees in Computer Science and Economics from Peking University. She received the Best Paper Award at the ICLR Workshop on Secure Machine Learning Systems, was invited to participate in the EECS Rising Stars Workshop, and was recognized as a Rising Star in Machine Learning/Data Science by multiple institutions.

I am Assistant Instructional Professor of GIScience at the Center for Spatial Data Science, University of Chicago. My research is situated at the intersection of data, computation, and human values, from which I aim to critically engage with geospatial computing both practically and theoretically. My research aims to answer the question: How can we develop computational methods, which are increasingly complex, to embody social responsibility, preserve privacy, and be used to understand and mitigate inequalities and injustices? My work has been focused on issues such as:

• Fairness in spatial computing: Integrating fairness, equitability, and other social justice principles into spatial optimization and machine learning to address the needs of individuals and communities with varying levels of vulnerability.
• Digital privacy: Developing methods to protect individual privacy while providing useful geospatial data.
• Computing for social good: Exploring how new geospatial computing methods can help understand and reduce inequalities and improve quality of life.

Education

• Ph.D. in Geography, The Ohio State University, 2023
• M.A. in Geography, The Ohio State University, 2022
• B.S. in Geographic Information Science, Wuhan University, 2019

Pedro Lopes is an Assistant Professor in Computer Science at the University of Chicago, where he leads the Human Computer Integration lab. Lopes’ research group focuses understanding how to integrate computer interfaces with the human body—creating the interface paradigm that supersedes wearable computing. They created wearable muscle stimulation devices that enable, for example, to: a user to manipulate a tool they never seen before, accelerate our reaction time, read and write information without using a screen, and transform someone’s arm into a plotter so they can solve complex problems with pen and paper. Their work is published at top-tier conferences (ACM CHI, ACM UIST, Cerebral Cortex). Pedro and his students have received one Best Paper award, three Best Talk Awards and two Best Paper nominations. Their work also captured the interest of media, such as MIT Technology Review, NBC, Discovery Channel, NewScientist, Wired and has been shown at Ars Electronica and World Economic Forum. (More: https://lab.plopes.org)

Ken Nakagaki is an interaction designer and HCI (Human-Computer Interaction) researcher from Japan. He is an assistant professor at the University of Chicago, Computer Science Department. He directs ‘Actuated Experience Lab’ [AxLab] there.

His research has been focuses on inventing novel user interface technologies that seamlessly combine dynamic digital information or computational aids into daily physical tools and materials. He is passionate about creating novel physical embodied experiences using such interfaces through curiosity-driven tangible prototyping processes. During his PhD study at MIT Media Lab, he pursued research in Actuated Tangible User Interfaces, under supervision of Prof. Hiroshi Ishii.

Before joining the Media Lab, he received Master’s and Bachelor’s degrees in interaction design from Keio University. His research has been presented in top HCI conferences (ACM CHI, UIST, TEI). His works were also demonstrated in international exhibitions and museums such as the Ars Electronica Festival and Laval Virtual. He has received numerous awards, including the MIT Technology Review’s Innovators Under 35 Japan & Asia Pacific, the Japan Media Arts Festival, and the James Dyson Award.

Website: https://www.ken-nakagaki.com/about.

Brian Nord uses artificial intelligence to search for clues on the origins and development of the universe. He actively works on statistical modeling of strong gravitational lenses, the cosmic microwave background, and galaxy clusters. As leader of the Deep Skies Lab, he brings together experts in computer science and technology to study questions of cosmology, including dark energy, dark matter, and the early universe, through large-scale data analysis.

Nord has authored or co-authored nearly 50 papers. He trains scientists in public communication, advocates for science education and funding, and works to develop equitable and just research environments. As co-leader of education and public engagement at the Kavli Institute for Cosmological Physics at UChicago, he organizes Space Explorers, a program to help underrepresented minorities in high school engage in hands-on physics experiences outside the classroom. He is an associate scientist at Fermi National Accelerator Laboratory, where he is a member of the Machine Intelligence Group.

Homepage.

Monica Rosenberg joined the faculty of The University of Chicago in 2019. She is an Associate Professor in the Department of Psychology and member of the Committees on Computational Neuroscience and Neurobiology and the Institute for Mind and Biology. Her research explores how we pay attention and how insights from attention research can help improve focus. Dr. Rosenberg completed her PhD and postdoctoral work in the Department of Psychology at Yale University after earning her undergraduate degree in cognitive neuroscience at Brown University.

A primary focus of Dr. Rosenberg’s work has been what we can learn about a person from their unique patterns of brain activity and what this can tell us about the nature of the brain and mind. In particular, she builds models that predict attention and cognition from functional neuroimaging data. This work has revealed, for example, that data collected while a person is simply resting in an MRI scanner (and not completing any task at all) can be used to predict aspects of their behavior, including how well they pay attention and remember information. Dr. Rosenberg’s work also uses behavioral, neuroimaging, and machine learning techniques to investigate how attention fluctuates over time, changes across development, and interacts with the rest of the mind.

I am an Assistant Professor of Computer Science at the University of Chicago. I received my PhD in Computer Science from Yale University in 2020. My current research explores social dynamics in human-robot interactions, where a robot’s social behaviors lead to positive outcomes for people (e.g., improved team dynamics and performance in a human-robot team, educational learning outcomes for children). During my PhD, I focused on developing robots that improve the performance of human-robot teams by shaping team dynamics to promote inclusion, trust, and cohesion.

Chenhao Tan is an associate professor at the Department of Computer Science and the UChicago Data Science Institute. His main research interests include language and social dynamics, human-centered machine learning, and multi-community engagement. He is also broadly interested in computational social science, natural language processing, and artificial intelligence.

Website

Anna is a Research Data Scientist at the University of Chicago’s Data Science Institute. Her recent work develops methodologies in machine learning for applied problems in medicine, with a particular emphasis on using unsupervised learning of representations to understand image content and structure. She is applying these methods to problems related to understanding and predicting cancer risk, designing personalized cancer screening policies, and designing clinical trials to evaluate AI systems. Previously, Anna served as a postdoctoral fellow at the Data Science Institute, where she was affiliated with the groups of Michael Maire in Computer Science and Olufunmilayo Olopade in the Department of Medicine. She earned her PhD in Physics from the University of Notre Dame, where Kevin Lannon advised her.

Dr Jai Yu’s research focuses on understanding the neurophysiological mechanisms that support the formation of knowledge. His lab records and analyzes neural data. Prior to joining the University of Chicago, he worked as a data scientist at a Silicon Valley neuromodulation startup where he used advanced data analytics to guide the development of devices for treating neurological conditions.